KR101582676B1 - temperature control apparatus of heating and cooling mold using extended kalman filter - Google Patents
temperature control apparatus of heating and cooling mold using extended kalman filter Download PDFInfo
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- KR101582676B1 KR101582676B1 KR1020140080066A KR20140080066A KR101582676B1 KR 101582676 B1 KR101582676 B1 KR 101582676B1 KR 1020140080066 A KR1020140080066 A KR 1020140080066A KR 20140080066 A KR20140080066 A KR 20140080066A KR 101582676 B1 KR101582676 B1 KR 101582676B1
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Abstract
The present invention relates to a temperature controller of a heating and cooling mold apparatus to which an extended Kalman filter is applied. The temperature controller includes a cooling and heating synchronous unit provided for heating and cooling the inside of the mold, a driving unit for driving the cold and hot synchronous motors, And a control unit for controlling the temperature of the mold based on the information output from the temperature sensor unit and the temperature control profile recorded in the storage unit, wherein the temperature control profile is a molding temperature control condition for the resin injected into the mold, And a temperature control unit for generating a temperature adjustment calculation value by the Kalman filter extended to follow the profile and controlling the drive of the drive unit in accordance with the generated temperature adjustment calculation value, The first thermoelectric heaters being arranged so as to be spaced apart from each other in a horizontal plane relative to the first thermoelectric heaters, And second thermoelectric heaters disposed between the first thermoelectric heaters and spaced apart from each other at a position spaced apart from the liver. According to the temperature controller of the mold apparatus, it is possible to perform precise temperature control, thereby increasing the yield rate and enhancing the stability against temperature control due to strong ability to deal with disturbance.
Description
The present invention relates to a temperature controller of a heating and cooling mold apparatus to which an extended Kalman filter is applied. More specifically, the present invention relates to a temperature controller using an extended Kalman filter To a temperature controller of the device.
Generally, in a mold for injection molding of a synthetic resin, the temperature of the mold is precisely controlled in order to improve the appearance of the product to be molded. If the temperature of the mold can not be precisely controlled, the appearance, electrical characteristics, shrinkage ratio, dimensions and the like of the product are considerably affected, resulting in a large number of molding defects.
The mold temperature control mainly uses a method of circulating a heater or heated water to the inside of the mold to raise the mold temperature, and conversely, cooling the mold with a fan or cooling water.
The temperature control system for controlling the temperature of the mold is variously disclosed in Korean Patent No. 10-0361477.
However, in the conventional mold temperature control method, the PID control method is used to simply control the information measured from the sensor for detecting the temperature of the mold to the target value, so that the measurement error of the sensor part and the measurement error of the system can not be reflected, There is a disadvantage that precision is lowered.
SUMMARY OF THE INVENTION It is an object of the present invention to provide a temperature controller for a mold apparatus which can precisely control the temperature of an injection molding die apparatus by applying an extended Kalman filter.
In order to accomplish the above object, a temperature controller of a mold apparatus according to the present invention includes: a cold / hot synchronous motor installed to heat and cool an inside of a mold; A driving unit for driving the hot / cold synchronicity; A temperature sensor unit for detecting a temperature in the mold; A storage unit in which a temperature control profile, which is a molding temperature control condition for a resin injected into the mold, is recorded; A temperature adjustment calculation value is generated by a Kalman filter extended so that the temperature of the mold follows the temperature control profile using the information output from the temperature sensor unit and the temperature control profile recorded in the storage unit, And a temperature control unit for controlling driving of the driving unit according to the adjustment calculation value,
A hot / cold stirrer for heating and cooling the inside of the mold;
A driving unit for driving the hot / cold synchronicity;
A temperature sensor unit for detecting a temperature in the mold;
A storage unit in which a temperature control profile, which is a molding temperature control condition for a resin injected into the mold, is recorded;
A temperature adjustment calculation value is generated by a Kalman filter extended so that the temperature of the mold follows the temperature control profile using the information output from the temperature sensor unit and the temperature control profile recorded in the storage unit, And a temperature control unit for controlling driving of the driving unit according to the adjustment calculation value,
The cold /
The first thermoelectric heaters being arranged so as to be spaced apart from each other in a horizontal plane with respect to a molding space into which the resin of the metal mold is injected; And second thermoelectric heaters disposed between the first thermoelectric heaters.
Preferably, the cold / hot movable unit includes a cooling unit controlled by the driving unit and capable of supplying cooling water through a cooling path formed in the metal mold.
In addition, the temperature control unit controls the temperature of the mold by the Kalman filter extended to follow the temperature control profile using the temperature information output from the temperature sensor unit and the temperature control profile provided by the storage unit An EKF PID controller for generating an operation value; And a PWM generator for outputting a drive pulse corresponding to a temperature adjustment operation value output from the EKF PID controller, wherein the drive unit switches on / off according to a drive pulse output from the PWM generator, And a switching unit for supplying power.
The EKF PID controller inputs a temperature value measured from the temperature sensor unit and a follow-up value of the temperature control profile into a state equation, calculates a Kalman gain through an extended Kalman filter of the input state equation, To output a temperature adjustment operation value to be applied to the driving unit by digital PID control, and performs temperature control while feeding back the presently calculated value to an initial value.
According to the temperature controller of the mold apparatus of the present invention, it is possible to perform precise temperature control, thereby increasing the yield and increasing the stability of the temperature control due to the ability to cope with disturbance.
1 is a schematic view of a temperature controller of a mold apparatus according to the present invention,
FIG. 2 is a view showing a system for controlling an electric heater incorporated in the mold of FIG. 1,
FIG. 3 is a diagram showing a system for performing cooling control on the mold of FIG. 1,
FIG. 4 is a diagram illustrating a process of performing an operation in the EKF PID controller of FIG. 2,
FIG. 5 is a block diagram showing a module for performing an arithmetic process in the EKF PID controller of FIG. 2,
6 is a diagram schematically showing a boundary condition interface for explaining the present invention.
Hereinafter, a temperature controller of a mold apparatus according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a view schematically showing a temperature controller of a mold apparatus according to the present invention. FIG. 2 is a view showing a system for controlling an electric heater incorporated in the mold of FIG. 1, and FIG. 3 is a cross- Fig. 2 is a diagram showing a system for performing cooling control on a cooling medium.
1 to 3, a
The
The first
The first and second thermal
The
The
When the temperature is lowered, the driving
Here, the
The driving
The driving
The
The
It is a matter of course that the
The
Here, the temperature control profile refers to the information in which the target temperatures, i.e., the heating temperature and the cooling temperature, over time are recorded for molding the resin injected into the
The
The
The
Further, the
The PWM (Pulse Width Modulation)
Here, the
The
In addition, the
Hereinafter, the control process of the EKF PID controller 131 will be described in more detail.
The Kalman filter is proposed by Rudolf E. Kalman as known in the art, and is based on a time-dependent (nonlinear) time-varying time series with noisy equations of motion using the Least Square Method state vector) that is a recursive computational solution.
The Kalman filter is used to find a signal from noise so that one system can adequately predict changes over time.
Linear system is a system which can express the equation modeled by linear operators. It is not easy to find such a linear system in real environment. In many cases, it is not easy to use Extended Kalman Filter (EKF) or UKF Unscented Kalman Filter) is often used.
In order for the Kalman filter to remove noise from the extracted signal, the process currently being modeled must be capable of being described as a linear system.
KF is a filter for predicting linear systems. Mathematically, KF minimizes errors that can occur by predicting the state of a linear system.
When describing a linear system, it is generally expressed in the following two simple formulas.
The state equation (Sate Equation) represents the overall signal waveform of the system and is called Process Model, Plant Model, and so on.
The output equations represent the measurable values of the system signals and are called the measurement model and the sensor model and can be expressed by the following equation (2).
Where A, B, and C are determinants, k is a sampling time, x is a state vector (Vector) representing the state of the system, and z is an input.
Also,
The system noise covariance Q and the measurement noise covariance R matrix vary with each sampling time, but are assumed to be constant.
The m * m matrix A of the state equation moves from the past sampling time k to the current sampling time k + 1 when there is no input or process noise. A is assumed to be a constant constant though it changes at every sampling time.
When the basic operation of the Kalman filter becomes possible, the sensitivity and response characteristics of the KF can be changed by adjusting the values of the covariance matrix indicating the noise for self-tuning.
Since the Kalman filter is designed to estimate the state variables in a linear model, if the model is nonlinear, a linearization procedure must be performed in deriving the filter equation.
In the linearization process, the Taylor series is used, but if the inherent properties of the nonlinear model are lost, it becomes impossible to apply them. The Kalman filter obtained here is called an extended Kalman filter (EKF).
The extended Kalman filter applied in the present invention is an optimal state estimation algorithm for a nonlinear system that performs a recursive operation. For recursive operations, the extended Kalman filter must be applied in the form of discrete-time state-space equations. Therefore, the equation must be transformed into a discrete time form.
The extended Kalman filter is a form of estimating the state using the form of feedback control and can be thought of as divided into a time update equation and a measurement update equation. The time update equation is obtained by using the previous state and the error covariance, And therefore it is constituted by a prediction and correction algorithm to form a cycle.
The time and measurement update pairs are reflected and applied after the new previous estimate and are repeated in a recursive fashion, one of the characteristics of the Kalman filter.
The measured noise covariance R and the system noise covariance Q must be obtained through self-tuning since direct observation at the beginning of the estimation system is difficult. The tuning is usually performed off-line, but in the proposed system, the initial condition due to the increase in the initial heating temperature has a linear characteristic, so it does not need to have a separate initial value.
In the condition that Q and R are real constants, the measurement error covariance
Accurate heating-cooling system models are essential to improve large errors in estimates when applying extended parameters to certain Kalman filters.
In the extended Kalman filter algorithm, the influence of the system noise that deteriorates the accuracy of the estimation in the error propagation step can be seen.
Filtering is the process of extracting the noise of the signal. If the signal and the noise spectra do not overlap, only the noise component is attenuated, and the design of the filter to pass only the required signal becomes very easy. In this case, signals such as a low-pass filter, a band-pass filter, and a high-pass filter are separated from noise, and only relevant frequencies are passed through.
The extended Kalman filter can be applied to the controller of the heating-cooling mold device. It is the fact that the one-pass algorithm is faster than the circulation algorithm, , The complexity does not increase.
In addition, by estimating the state change itself, the extended Kalman filter is not limited to time-invariant, and it is possible to conduct observations with high precision by the iterative algorithm method.
The
The EKF control method is known to have high convergence speed and accuracy in parameter estimation compared to other control algorithms, and the motion model of the controlled system is a nonlinear model including unknown parameters. By using the EKF algorithm as an error estimation algorithm, it can be applied to both linear and nonlinear systems, and the effect of the filter on the noise involved in measuring the output signal can be obtained.
The EKF reduces the noise included in the state variable measurement and estimates the state variable. However, applying EKF can also estimate unknown plant parameters. Applying this function to the tuning of the PID control gain can give better performance than the controller with fixed PID gain.
The
The heat-cooling mold system leading the high-gloss mold technology can obtain the optimal temperature profile through experience and trial and error of various mold technology and apply the optimal temperature control profile for each product through the secured control profile do.
The
In addition, the
Also, the
The
There are four processor variables associated with temperature. The first is the melting temperature of the polymer material, which is the molding material, and this temperature is not directly measured in the process. The melting temperature of the polymer in the cylinder determines the selection of other process parameters and the setting of the device.
The second is the surface temperature of each mold at the temperature of the mold (10), and the material melts every cycle and enters the mold (10). Mold (10) temperature is the result of many processes and design variables. The melting temperature and the cooling time ratio can be determined and reflected in the design of the cooling circuit in the
The heat conduction at the surface in the selected direction by the convection boundary condition due to the heating of the
The meaning of time t → ∞ means that the system is approaching the new equivalent state, which means it reaches the new steady state.
The heat transfer equation from conduction to convection is as follows.
In an interface boundary condition, the contact of two objects must have the same temperature in the same area, and the contact on the surface does not store energy, so the heat flux on both sides of the interface must be the same. Based on these conditions, the condition of the interface boundary condition is expressed by Equation (7).
The steady-state heat conduction differential equation on the plane (two-dimensional) or space (three-dimensional) where the position is determined by the position with respect to the intersecting axis, that is, on the x and y axes is shown in Equation 8 below.
Integrating Equation (8) yields Equation (9) below.
Integrating equation (9) again yields equation (10).
Where T (x) is a general solution and C1 and C2 are arbitrary constants. The temperature condition at the boundary condition is expressed by Equation (11) below.
Applying the boundary conditions through the general solution,
Substituting T1 so as not to include T (x), x after the boundary condition is applied is expressed by Equation (13).
Equation (13) is summarized as Equation (14) below.
The only solution for the general solution is shown in Equation 15 below.
In addition, the optimal solution for the majority is expressed by the following equation (16).
The one-dimensional steady-state heat conduction equation is expressed by Equation (17) below.
Integrating Equation 17 twice yields Equation 18 below.
1 = C2 and Ts, 2 = C1L + C2 = 0 for a condition where x = 0 and x = L are applied to T (0) = Ts, 1 and T C1L + Ts, 1, so that T (x) can be summarized as follows.
The concept of heat resistance is used to define the flow of heat through the analytical identity between heat and electrical resistance.
In the mold apparatus, only conduction is considered among the heat transfer methods of radiation, convection, and conduction, and the ratio of heat conduction is expressed by the following equation (20).
The temperature drop ratio of the thermal resistance through any layer is constant and is proportional to the thermal resistance of the layer. The large thermal resistance has a large temperature drop, which can be expressed by the following equation (21).
The temperature drop across this optional layer is equal to the heat transfer time of the heat resistance through this layer.
The steady-state problem for heat conduction is briefly described, and the governing equations are expressed as linear heat flow equations in two dimensions and are shown in Equation 22 below.
here
Where K is the conductivity,
Where Q is the total heat generated,
D for heat flow
Where c is the unit of heat in a particular material
In a thin steel shell, the temperature due to heat conduction is determined by the 1-D transient heat conduction equation, which can be applied to the chain for heat-dependent thermal conduction as shown in Equation 24 below.
here
Evangelize
Latent Heat
The standard Kalman filter is based on linear differential equations,
The nonlinear function, f,
State vector according to measurement
In the measurement equation, the nonlinear function, h,
here
In the linear model, the equation of state in matrix form is as follows.
here
The Kalman filter targets the linear state model and the time update equation is the time step
Subscripts marked with - are predictive values, and subscripts without - indicate operational values.
here,
Here, I is a unitary matrix.
Extended Kalman Peter is an algorithm that can extend the Kalman filter to nonlinear systems. Linearization The key element of the Kalman filter is the linearization model of a nonlinear system, and an excellent linearization model results in superior performance. Considering the nonlinear model resulting from the system model, the following equations (39) and (40) are obtained.
Using this relationship, the initial step
Next, calculate the Kalman gain.
Measures
When the error covariance calculation is performed, the following equation 45 is obtained.
And the calculated error covariance is fed back to the initial value, and the error value is corrected while the calculation process is repeated.
This process is illustrated in FIG.
On the other hand, the system matrices A and H derive determinants using the system model.
here,
The state model X is summarized as follows.
here
The output Y is summarized as Equation (48) below.
The control state function U can be summarized as Equation 49 below.
here,
For example, assuming that the measured temperature is 250 ° C and the error is ± 5 ° C, assuming a sensor with a 5% error range, it will be in the range of 12.5 ° C. Considering the error, actual temperature exists within the range of 245 ~ 255 ℃ and it can be estimated through Kalman filter. Assuming that the estimated value is G, the measured value is M, and the optimal calculated value is E, the weight can be calculated as shown in the following Equation 47. " (47) " The weight W can be obtained by adding the measured value and the error value and dividing it by the range of the error value.
The variance estimate can be defined as the value obtained by adding the error and the measured value and dividing the error and the measured value. By performing the iterative operation through this value, the error can be corrected and utilized.
In the present invention, the temperature information obtained through the
An output of the proportional-integrator is derived from the measured temperature change amount through the
4, the input value of the extended Kalman filter is denoted by z (k) as a measured value, and
5 is a block diagram of an operation module for performing an operation process in the EKF PID controller.
This process further processes the data by reflecting the extended Kalman filter, that is, the system model for the nonlinear model, to the process proposed by Professor Kalman.
The
110: heater part 120: cooling part
130: driving unit 140: temperature sensor unit
150: storage unit 160: temperature control unit
Claims (4)
A driving unit for driving the hot / cold synchronicity;
A temperature sensor unit for detecting a temperature in the mold;
A storage unit in which a temperature control profile, which is a molding temperature control condition for a resin injected into the mold, is recorded;
A temperature adjustment operation value is generated by a Kalman filter extended so that the temperature of the mold follows the temperature control profile using the information output from the temperature sensor unit and the temperature control profile recorded in the storage unit, And a temperature control unit for controlling driving of the driving unit according to the adjustment calculation value,
The cold /
The first thermoelectric heaters being spaced apart from each other in a horizontal plane with respect to a molding space into which the resin of the mold is injected; And second thermoelectric heaters disposed between the first thermoelectric heaters,
The cold /
And a cooling unit controlled by the driving unit to supply cooling water through a cooling path formed in the mold,
The cooling unit
A tank in which cooling water is accommodated;
A transfer tube for interconnecting the tank and the cooling furnace of the mold;
A pump installed on the transfer pipe;
An air supply unit installed to supply air through the transfer pipe;
And a switching valve installed on the transfer pipe for connecting the cooling passage to the air supply unit or the tank,
Wherein the drive unit controls the switching valve to be connected to the tank when the temperature is lowered so that the cooling water stored in the tank is supplied to the cooling path of the metal mold and when the temperature rises again, So that air is supplied from the air supplier to the cooling furnace to discharge the cooling water in the cooling path of the mold, the electric heater is operated,
The temperature control unit
An EKF PID for generating a temperature adjustment operation value by the Kalman filter extended so that the temperature of the mold follows the temperature control profile using the temperature information output from the temperature sensor unit and the temperature control profile provided from the storage unit, A controller;
And a PWM generator for outputting a drive pulse corresponding to a temperature adjustment computation value output from the EKF PID controller,
And a switching unit for supplying power corresponding to the electric heater while switching on / off according to a driving pulse output from the PWM generator,
The EKF PID controller
A temperature value measured from the temperature sensor unit and a follow-up value of the temperature control profile are input to a state equation, a Kalman gain is calculated using an extended Kalman filter as an input state equation, and a digital PID And outputs the temperature adjustment operation value to be applied to the driving unit by the control, and performs the temperature control while feeding back the presently calculated value to the initial value.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108688114A (en) * | 2018-06-12 | 2018-10-23 | 浙江工业大学 | It is a kind of quickly to become mould temperature injection molding forming method and its former |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05250047A (en) * | 1992-03-09 | 1993-09-28 | Sumitomo Heavy Ind Ltd | Metal die temperature adjusting device for injection molding machine |
| JP3229734B2 (en) * | 1993-09-30 | 2001-11-19 | 関東自動車工業株式会社 | Temperature control device for injection mold |
| KR20090101006A (en) * | 2008-03-21 | 2009-09-24 | (주) 천복금형 | Core unit for preventing weldline and injection mold using the same |
| CN103064287A (en) * | 2012-11-14 | 2013-04-24 | 山东交通职业学院 | Kalman filtering method applied to temperature control of taper type double-screw extruder |
-
2014
- 2014-06-27 KR KR1020140080066A patent/KR101582676B1/en active IP Right Grant
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05250047A (en) * | 1992-03-09 | 1993-09-28 | Sumitomo Heavy Ind Ltd | Metal die temperature adjusting device for injection molding machine |
| JP3229734B2 (en) * | 1993-09-30 | 2001-11-19 | 関東自動車工業株式会社 | Temperature control device for injection mold |
| KR20090101006A (en) * | 2008-03-21 | 2009-09-24 | (주) 천복금형 | Core unit for preventing weldline and injection mold using the same |
| CN103064287A (en) * | 2012-11-14 | 2013-04-24 | 山东交通职业学院 | Kalman filtering method applied to temperature control of taper type double-screw extruder |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108688114A (en) * | 2018-06-12 | 2018-10-23 | 浙江工业大学 | It is a kind of quickly to become mould temperature injection molding forming method and its former |
| CN108688114B (en) * | 2018-06-12 | 2023-09-12 | 浙江工业大学 | Quick temperature-changing injection molding method and molding equipment thereof |
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